BACKGROUND: Health promotion programs for the workplace are often sold to employers with the promise that they will pay for themselves with lowered health care expenditures and reduced absenteeism. In a recent review of the literature, it was noted that analysts often caution not to expect a positive return on investment until the third year of operation. OBJECTIVE: This study investigates whether a positive return on investment was generated in the third year for the health promotion program used by the University of Minnesota. It further investigates what it is about the third year that would explain such a phenomenon. MEASURES: The study uses health care expenditure data and absenteeism data from 2004 to 2008 to investigate the effect of the University's lifestyle and disease management programs. It also investigates the effectiveness of participation in Minnesota's 10,000 Steps walking program and Miavita self-help programs. RESEARCH DESIGN: A differences-in-differences equations approach is used to address potential selection bias. Possible regression to the mean is dealt with by using only those who were eligible to participate as control observations. Propensity score weighting was used to balance the sample on observable characteristics and reduce bias due to omitted variables. RESULTS: The study finds that a 1.76 return on investment occurs in the third year of operation that is generated solely by the effect of disease management program participation in reducing health care expenditures. However, neither of the explanations for a third-year effect we tested seemed to be able to explain this phenomenon.

The health insurance loading fee represents the portion of the premium above the expected amount of medical care expenditures paid by the insurance company. The size of the loading fees and how they vary by employer group size have important implications for health policy given the recent passage of the Patient Protection and Affordable Care Act. Despite their policy relevance, there is surprisingly little empirical evidence on the magnitude and the determinants of health insurance loading fees. This paper provides estimates of the loading fees by firm size using data from the confidential Medical Expenditure Panel Survey Household Component-Insurance Component Linked File. Overall, we find an inverse relationship between employer group size and loading fees. Firms of up to 100 employees face similar loading fees of approximately 34%. Loads decline with firm size and are estimated to be on average 15% for firms with more than 100 employees, but less than 10,000 employees, and 4% for firms with more than 10,000 workers.

Following the recent introduction of an incentive-based, exercise-focused wellness program at a large public university, this paper investigates the factors that influence employees' behavior with respect to participation and regular exercise. Results suggest that an employee's probability of signing up for the program is related to her exercise behavior prior to the program's inception, the time cost of exercise, taste for fitness center exercise, and attitudes about the benefits and barriers of exercise. Employees who are older, male, and were regular fitness center exercisers prior to the program's inception are more likely to be regular exercisers.

CONTEXT Provisions of the Patient Protection and Affordable Care Act of 2010 (PPACA) expand Medicaid to all individuals in families earning less than 133 percent of the federal poverty level (FPL) and make available subsidies to uninsured lower-income Americans (133 to 400 percent of FPL) without access to employer-based coverage to purchase insurance in new exchanges. Since primary care physicians typically serve as the point of entry into the health care delivery system, an adequate supply of them is critical to meeting the anticipated increase in demand for medical care resulting from the expansion of coverage. This article provides state-level estimates of the anticipated increases in primary care utilization given the PPACA's provisions for expanded coverage. METHODS Using the Medical Expenditure Panel Survey, this article estimates a multivariate regression model of annual primary care utilization. Using the model estimates and state-level information regarding the number of uninsured, it predicts, by state, the change in primary care visits expected from the expanded coverage. Finally, the article predicts the number of primary care physicians needed to accommodate this change in utilization. FINDINGS This expanded coverage is predicted to increase by 2019 the number of annual primary care visits between 15.07 million and 24.26 million. Assuming stable levels of physicians' productivity, between 4,307 and 6,940 additional primary care physicians would be needed to accommodate this increase. CONCLUSIONS The PPACA's health insurance expansion parameters are expected to significantly increase the use of primary care. Two strategies that policymakers may consider are creating stronger financial incentives to attract medical school students to primary care and changing the delivery of care in ways that lead to operational improvements, higher throughput, and better quality of care.

BACKGROUND: Research into nursing home transitions has given limited attention to the facility or community contexts. OBJECTIVE: To identify facility and market factors affecting transitions of nursing home residents back to the community. RESEARCH DESIGN: Multilevel models were used to estimate effects of facility and market factors on facility-level community discharge rates after controlling for resident demographic, health, and functional conditions. Facility discharge rates were adjusted using Empirical Bayes estimation. SUBJECTS: Annual cohort of first-time admissions (N=24,648) to 378 Minnesota nursing facilities in 75 nursing home markets from July 2005 to June 2006. MEASURES: Community discharge within 90 days of admission; facility occupancy, payer mix, ownership, case-mix acuity, size, admissions from hospitals, nurse staffing level, and proportion of admissions preferring or having support to return to the community; and nursing market population size, average occupancy, market concentration, and availability of home and community-based services. RESULTS: Rates of community discharge (Empirical Bayes residual) were highest in facilities with more residents preferring community discharge, more Medicare days, higher nurse staffing levels, and higher occupancy. In addition, facilities had higher community discharge rates if they were located in markets with a greater ratio of home and community-based services recipients to nursing home residents and with larger populations. CONCLUSIONS: State Medicaid programs should undertake system-level interventions that encourage nursing facilities to reduce unused bed capacity, balance the mix of payers, invest in nurse staffing, and take other steps to promote community discharges. In addition, states should increase home and community-based services, particularly in markets with low community discharge rates.

OBJECTIVE To provide state-level estimates of the size and structure of the US individual market for health insurance and to investigate the potential impact of new medical loss ratio (MLR) regulation in 2011, as indicated by the Patient Protection and Affordable Care Act (PPACA). STUDY DESIGN Using data from the National Association of Insurance Commissioners, we provided state-level estimates of the size and structure of the US individual market from 2002 to 2009. We estimated the number of insurers expected to have MLRs below the legislated minimum and their corresponding enrollment. In the case of noncompliant insurers exiting the market, we estimated the number of enrollees that may be vulnerable to major coverage disruption given poor health status. RESULTS In 2009, using a PPACA-adjusted MLR definition, we estimated that 29% of insurer-state observations in the individual market would have MLRs below the 80% minimum, corresponding to 32% of total enrollment. Nine states would have at least one-half of their health insurers below the threshold. If insurers below the MLR threshold exit the market, major coverage disruption could occur for those in poor health; we estimated the range to be between 104,624 and 158,736 member-years. CONCLUSION The introduction of MLR regulation as part of the PPACA has the potential to significantly affect the functioning of the individual market for health insurance.

Significant investments have been made in developing and disseminating health care provider cost and quality information on the Internet with the expectation that stronger consumer engagement will lead consumers to seek providers who deliver high-quality, low-cost care. However, prior research shows that the awareness and use of such information is low. This study investigates how the information search process may contribute to explaining this result. The analysis reveals that the Web sites most likely to be found by consumers are owned by private companies and provide information based on anecdotal patient experiences. Web sites less likely to be found have government or community-based ownership, are based on administrative data, and contain a mixture of quality, cost, and patient experience information. Searches for information on hospitals reveal more cost and quality information based on administrative data, whereas searches that focus on clinics or physicians are more likely to produce information based on patient narratives.

OBJECTIVE To identify factors associated with small group employer participation in New Mexico's State Coverage Insurance (SCI) program. DATA SOURCES Telephone surveys of employers participating in SCI (N=269) and small employers who inquired about SCI (N=148) were fielded September 2008-January 2009. STUDY DESIGN Descriptive and multivariate analyses investigated differences between employer samples, including employer characteristics, concerns that applied to the business when deciding whether to participate in SCI, prior offerings of insurance to workers, and perceived affordability of the program. DATA COLLECTION/EXTRACTION METHODS Unweighted employer samples yielded 88 and 75 percent response rates for the participating and inquiring employers, respectively. PRINCIPAL FINDINGS The administrative issue most commonly selected by inquiring employers as applying to their business was difficulty understanding how eligibility requirements applied to their business and its employees (53.5 percent). Inquiring businesses were significantly more likely to report concern about affording to pay the premiums in the first month (35.6 versus 18.7 percent) and the cost to the business over the long run (46.5 versus 26.6 percent) relative to participating employers. From the model results, businesses with the fewest full-time employees (zero to two) were 19 percentage points less likely to participate relative to businesses with six or more full-time employees. CONCLUSIONS Administrative and cost barriers to participation in SCI reported by employers suggest that the tax credit offered to small businesses under new federal provisions, which merely offsets the employer portion of premium, could be more effective if accompanied by additional supports to businesses.

Significant investments have been made in developing and disseminating health care provider cost and quality information on the Internet with the expectation that stronger consumer engagement will lead consumers to seek providers who deliver high-quality, low-cost care. However, prior research shows that the awareness and use of such information is low. This study investigates how the information search process may contribute to explaining this result. The analysis reveals that the Web sites most likely to be found by consumers are owned by private companies and provide information based on anecdotal patient experiences. Web sites less likely to be found have government or community-based ownership, are based on administrative data, and contain a mixture of quality, cost, and patient experience information. Searches for information on hospitals reveal more cost and quality information based on administrative data, whereas searches that focus on clinics or physicians are more likely to produce information based on patient narratives.

CONTEXT Provisions of the Patient Protection and Affordable Care Act of 2010 (PPACA) expand Medicaid to all individuals in families earning less than 133 percent of the federal poverty level (FPL) and make available subsidies to uninsured lower-income Americans (133 to 400 percent of FPL) without access to employer-based coverage to purchase insurance in new exchanges. Since primary care physicians typically serve as the point of entry into the health care delivery system, an adequate supply of them is critical to meeting the anticipated increase in demand for medical care resulting from the expansion of coverage. This article provides state-level estimates of the anticipated increases in primary care utilization given the PPACA's provisions for expanded coverage. METHODS Using the Medical Expenditure Panel Survey, this article estimates a multivariate regression model of annual primary care utilization. Using the model estimates and state-level information regarding the number of uninsured, it predicts, by state, the change in primary care visits expected from the expanded coverage. Finally, the article predicts the number of primary care physicians needed to accommodate this change in utilization. FINDINGS This expanded coverage is predicted to increase by 2019 the number of annual primary care visits between 15.07 million and 24.26 million. Assuming stable levels of physicians' productivity, between 4,307 and 6,940 additional primary care physicians would be needed to accommodate this increase. CONCLUSIONS The PPACA's health insurance expansion parameters are expected to significantly increase the use of primary care. Two strategies that policymakers may consider are creating stronger financial incentives to attract medical school students to primary care and changing the delivery of care in ways that lead to operational improvements, higher throughput, and better quality of care.

The health insurance loading fee represents the portion of the premium above the expected amount of medical care expenditures paid by the insurance company. The size of the loading fees and how they vary by employer group size have important implications for health policy given the recent passage of the Patient Protection and Affordable Care Act. Despite their policy relevance, there is surprisingly little empirical evidence on the magnitude and the determinants of health insurance loading fees. This paper provides estimates of the loading fees by firm size using data from the confidential Medical Expenditure Panel Survey Household Component-Insurance Component Linked File. Overall, we find an inverse relationship between employer group size and loading fees. Firms of up to 100 employees face similar loading fees of approximately 34%. Loads decline with firm size and are estimated to be on average 15% for firms with more than 100 employees, but less than 10,000 employees, and 4% for firms with more than 10,000 workers.

The health insurance loading fee represents the portion of the premium above the expected amount of medical care expenditures paid by the insurance company. The size of the loading fees and how they vary by employer group size have important implications for health policy given the recent passage of the Patient Protection and Affordable Care Act. Despite their policy relevance, there is surprisingly little empirical evidence on the magnitude and the determinants of health insurance loading fees. This paper provides estimates of the loading fees by firm size using data from the confidential Medical Expenditure Panel Survey Household Component-Insurance Component Linked File. Overall, we find an inverse relationship between employer group size and loading fees. Firms of up to 100 employees face similar loading fees of approximately 34%. Loads decline with firm size and are estimated to be on average 15% for firms with more than 100 employees, but less than 10,000 employees, and 4% for firms with more than 10,000 workers.